BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks

TL;DR

BiosERC leverages Large Language Models to incorporate speaker biographical information into emotion recognition in conversations, achieving state-of-the-art results across multiple benchmark datasets.

Contribution

This work introduces BiosERC, a novel framework that integrates speaker biographical data via LLMs into ERC tasks, enhancing model performance and generalization.

Findings

Achieved state-of-the-art results on IEMOCAP, MELD, and EmoryNLP datasets.

Effectively incorporates speaker attributes to improve emotion classification.

Demonstrates potential for adaptation to various conversation analysis tasks.

Abstract

In the Emotion Recognition in Conversation task, recent investigations have utilized attention mechanisms exploring relationships among utterances from intra- and inter-speakers for modeling emotional interaction between them. However, attributes such as speaker personality traits remain unexplored and present challenges in terms of their applicability to other tasks or compatibility with diverse model architectures. Therefore, this work introduces a novel framework named BiosERC, which investigates speaker characteristics in a conversation. By employing Large Language Models (LLMs), we extract the "biographical information" of the speaker within a conversation as supplementary knowledge injected into the model to classify emotional labels for each utterance. Our proposed method achieved state-of-the-art (SOTA) results on three famous benchmark datasets: IEMOCAP, MELD, and EmoryNLP, demonstrating the effectiveness and generalization of our model and showcasing its potential for adaptation to various conversation analysis tasks. Our source code is available at https://github.com/yingjie7/BiosERC.